






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     AutoConfig.from_pretrained(lowercase					)
  SCREAMING_SNAKE_CASE							:   str						    =     FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase					)
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     checkpoints.load_tax_checkpoint(lowercase					)

  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  if config.model_type == "t5":
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     "SelfAttention"
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    SCREAMING_SNAKE_CASE							:   str						    =     "LocalSelfAttention"
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    SCREAMING_SNAKE_CASE							:   Tuple						    =     "TransientGlobalSelfAttention"
  else:
    raise ValueError(
        "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
        " attribute with a value from ['local', 'transient-global]."					)

  # Encoder
  for layer_index in range(config.num_layers					):
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     F'''layers_{str(lowercase					)}'''

    # Self-Attention
    SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    SCREAMING_SNAKE_CASE							:   int						    =     tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      SCREAMING_SNAKE_CASE							:   Any						    =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    SCREAMING_SNAKE_CASE							:   int						    =     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE							:   int						    =     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    SCREAMING_SNAKE_CASE							:   Any						    =     flax_model.params["encoder"]["block"][str(lowercase					)]["layer"]
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_attention_key
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_attention_out
    SCREAMING_SNAKE_CASE							:   List[str]						    =     tax_attention_query
    SCREAMING_SNAKE_CASE							:   int						    =     tax_attention_value

    SCREAMING_SNAKE_CASE							:   str						    =     tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      SCREAMING_SNAKE_CASE							:   str						    =     tax_global_layer_norm

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_mlp_wi_a
      SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_mlp_wi_a
    else:
      SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_mlp_wi

    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_mlp_wo
    SCREAMING_SNAKE_CASE							:   Any						    =     tax_mlp_layer_norm

    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     flax_model_encoder_layer_block

  # Only for layer 0:
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
    SCREAMING_SNAKE_CASE							:   str						    =     tax_encoder_global_rel_embedding

  # Assigning
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers					):
    SCREAMING_SNAKE_CASE							:   str						    =     F'''layers_{str(lowercase					)}'''

    # Self-Attention
    SCREAMING_SNAKE_CASE							:   List[str]						    =     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE							:   str						    =     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    SCREAMING_SNAKE_CASE							:   str						    =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
    SCREAMING_SNAKE_CASE							:   Dict						    =     tax_enc_dec_attention_module["key"]["kernel"]
    SCREAMING_SNAKE_CASE							:   List[str]						    =     tax_enc_dec_attention_module["out"]["kernel"]
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_enc_dec_attention_module["query"]["kernel"]
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_enc_dec_attention_module["value"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      SCREAMING_SNAKE_CASE							:   Dict						    =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      SCREAMING_SNAKE_CASE							:   Dict						    =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      SCREAMING_SNAKE_CASE							:   Dict						    =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    SCREAMING_SNAKE_CASE							:   List[str]						    =     flax_model.params["decoder"]["block"][str(lowercase					)]["layer"]
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_attention_key
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tax_attention_out
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_attention_query
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_attention_value

    SCREAMING_SNAKE_CASE							:   Any						    =     tax_pre_attention_layer_norm

    SCREAMING_SNAKE_CASE							:   Any						    =     tax_enc_dec_attention_key
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_enc_dec_attention_out
    SCREAMING_SNAKE_CASE							:   Any						    =     tax_enc_dec_attention_query
    SCREAMING_SNAKE_CASE							:   Dict						    =     tax_enc_dec_attention_value

    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tax_cross_layer_norm

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_mlp_wi_a
      SCREAMING_SNAKE_CASE							:   int						    =     tax_mlp_wi_a
    else:
      SCREAMING_SNAKE_CASE							:   Tuple						    =     tax_mlp_wi

    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tax_mlp_wo

    SCREAMING_SNAKE_CASE							:   str						    =     txa_mlp_layer_norm

    SCREAMING_SNAKE_CASE							:   List[str]						    =     flax_model_decoder_layer_block

  # Decoder Normalization
  SCREAMING_SNAKE_CASE							:   int						    =     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  SCREAMING_SNAKE_CASE							:   List[str]						    =     txa_decoder_norm

  # Only for layer 0:
  SCREAMING_SNAKE_CASE							:   List[str]						    =     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_decoder_rel_embedding

  # Token Embeddings
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     tax_model["target"]["token_embedder"]["embedding"]
  SCREAMING_SNAKE_CASE							:   Tuple						    =     txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    SCREAMING_SNAKE_CASE							:   int						    =     tax_model["target"]["decoder"]["logits_dense"]["kernel"]

  flax_model.save_pretrained(lowercase					)
  print("T5X Model was sucessfully converted!"					)


if __name__ == "__main__":
   snake_case     					=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
   )
   parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
   parser.add_argument(
       """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
   )
   snake_case     					=		parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['pixel_values']
		def __init__(       self     :     List[str]     ,  snake_case__     :     bool = True     ,  snake_case__     :     Dict[str, int] = None     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[int, float] = 1 / 2_5_5     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     bool = True     ,  **snake_case__     :     List[Any]     ,  )      ->	None:
				super().__init__(**snake_case__		)
				_lowerCamelCase				   =  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  resample
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				_lowerCamelCase				   =  image_std if image_std is not None else OPENAI_CLIP_STD
				_lowerCamelCase				   =  do_convert_rgb
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Dict[str, int]     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Dict     ,  )      ->	np.ndarray:
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				_lowerCamelCase				   =  (size['height'], size['width'])
				return resize(snake_case__     ,  size=snake_case__     ,  resample=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[int, float]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	int:
				return rescale(snake_case__     ,  scale=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	np.ndarray:
				return normalize(snake_case__     ,  mean=snake_case__     ,  std=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     ImageInput     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Dict[str, int]] = None     ,  snake_case__     :     PILImageResampling = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[float] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     bool = None     ,  snake_case__     :     ChannelDimension = ChannelDimension.FIRST     ,  **snake_case__     :     str     ,  )      ->	PIL.Image.Image:
				_lowerCamelCase				   =  do_resize if do_resize is not None else self.do_resize
				_lowerCamelCase				   =  resample if resample is not None else self.resample
				_lowerCamelCase				   =  do_rescale if do_rescale is not None else self.do_rescale
				_lowerCamelCase				   =  rescale_factor if rescale_factor is not None else self.rescale_factor
				_lowerCamelCase				   =  do_normalize if do_normalize is not None else self.do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else self.image_mean
				_lowerCamelCase				   =  image_std if image_std is not None else self.image_std
				_lowerCamelCase				   =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				_lowerCamelCase				   =  size if size is not None else self.size
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  make_list_of_images(snake_case__		)

				if not valid_images(snake_case__		):
						raise ValueError(
						    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
						    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

				if do_resize and size is None or resample is None:
						raise ValueError('Size and resample must be specified if do_resize is True.'		)

				if do_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						_lowerCamelCase				   =  [convert_to_rgb(snake_case__		) for image in images]

				# All transformations expect numpy arrays.
				_lowerCamelCase				   =  [to_numpy_array(snake_case__		) for image in images]

				if do_resize:
						_lowerCamelCase				   =  [self.resize(image=snake_case__     ,  size=snake_case__     ,  resample=snake_case__		) for image in images]

				if do_rescale:
						_lowerCamelCase				   =  [self.rescale(image=snake_case__     ,  scale=snake_case__		) for image in images]

				if do_normalize:
						_lowerCamelCase				   =  [self.normalize(image=snake_case__     ,  mean=snake_case__     ,  std=snake_case__		) for image in images]

				_lowerCamelCase				   =  [to_channel_dimension_format(snake_case__     ,  snake_case__		) for image in images]

				_lowerCamelCase				   =  BatchFeature(data={'pixel_values': images}     ,  tensor_type=snake_case__		)

				return encoded_outputs